Automated autonomous driving aid systems of road vehicles have become increasingly present in today's vehicles. Various arrangements and methods have been developed to assist driver of a road vehicle and relieve the driver for at least some of the driving tasks.
Automated vehicles often obtain their semi-autonomous driving capabilities based on camera and radar sensors. Performances of such automated vehicles are dependent on a camera to detect lane marking on the road to enable automated active steering and radar is used to detect and measure distance to a lead vehicle for automated throttle control. If no lead vehicle is present the automated vehicle will lose its self-driving capabilities as soon as lane markings are not detected, e.g. being missing, worn out or covered by snow etc.
As a result, for road vehicles having semi-autonomous driving capabilities the driving experience becomes unpredictable. As the transitioning time from semi-autonomous to manual modes of driving is very short, often less than 1 second, a driver of the vehicle may be required to continuously monitor the system closely at all times, almost requiring the same mental effort from the driver as when driving manually, in order to be able to take over the task of driving if required.
In view of the above, there is a need for improving the predictability of transition requirements, to and from automated autonomous driving aid control systems.